Image processing system for compressing image data including binary image data and continuous tone image data by a sub-band transform method with a high-compression rate

ABSTRACT

An image processing system which compresses an image including both a binary image and a continuous tone image by a sub-band transform method with a high compression rate. A 2×2 pixel matrix block is extracted from image data. A transform factor having a plurality of frequency components is obtained from the 2×2 pixel matrix block data. The transform factor is quantized by a fixed-length quantizing method by deleting a predetermined number of lower order bits of each of the frequency components.

BACKGROUND OF THE INVENTION

[0001] 1. Field of the Invention

[0002] The present invention generally relates to an image datacompressing technique and, more particularly, to an image processingsystem which compresses and expands image data by using a sub-bandencoding method.

[0003] The image processing system related to the present invention maybe used in a digital copy machine, a facsimile machine, a digitalprinter, a digital camera or a digital video camera, and also may beused in an image recording system such as a CD ROM drive or a floppydisc drive.

[0004] 2. Description of the Related Art

[0005] A sub-band encoding method such as the discrete cosine transform(DCT) or the Harr Wavelet transform is considered a method foreffectively compressing a continuous tone image. Additionally, JapaneseLaid-Open Patent Application No. 2-305272 discloses another method forencoding image data by separating an image area into a character areaand a halftone area so as to encode these areas by an encoding methodappropriate for each of the areas.

[0006] Such a method for compressing image data using the sub-bandtransform such as the DCT or the Harr Wavelet transform can effectivelycompress a continuous tone image. However, there is a problem in that acompression rate is low when a complete binary image is compressed.

[0007] Additionally, in a digital copy machine, even if an originalimage is a complete binary image, image data obtained by scanning such acomplete binary image may become incomplete binary image data due tofluctuation in a scanning operation. Thus, there may be a problem incompressing such incomplete binary data by, an entropy-encoding methoddue to fluctuation in a scanned image.

[0008] As a method for rotating or sorting images in a copy machine, ablock truncation encoding (BTC) which is one of fixed length encodingmethods is popular. However, there is a problem in that a compressionrate for an entropy encoding is low as compared to that of a sub-bandtransform method, and a calculation is complex.

[0009] In an image forming apparatus such as a copy machine or aprinter, image data obtained by a scanner is subjected to gammacorrection or a filtering process so as to adjust image quality. Thethus-processed image data is stored in a memory, and then the image datais sent to a printing unit.

[0010] Generally, such image data is subjected to a data compression inorder to reduce a capacity of the memory that stores the processed imagedata. Generally, in a data compressing method, image data is transformedinto frequency components by using an orthogonal transformation such asthe discrete cosine transform (DCT), and the quantized image data issubjected to an entropy encoding. Dispersion of a high-frequency factorin the frequency transformation factors varies in response to amagnitude of change in intensity of the image. Thus, the image qualityis improved when a quantizing method is changed in response to a type ofan area to be processed.

[0011] Japanese Laid-Open Patent Application No. 7-74959 discloses atechnique in which a quantization table is changed based on a transformfactor obtained by an orthogonal transformation of an original image byeach individual block so that the image quality matches the contents ofthe image data and a compression rate is improved.

[0012] When an image is printed by a copy machine, a character image anda line image can be well recognized by rendering the intensity slope ofa contour of the characters or the lines to be steep. On the other hand,when an image having a gentle intensity slope such as a photograph isprinted, a random change in the intensity having a small amplitude issensed as a noise. Thus, it is preferred for such a photographic imageto reduce the intensity slope of an output image. Particularly, in amesh point photographic image, a better image quality can be obtained byreducing the intensity slope even for an area having a steep intensityslope.

[0013] Accordingly, an edge area corresponding to a character image or aline image is separated from a mesh point image and a gentle slope areaof a photographic image so that the edge area is subjected to adifferential filtering process whereas the photographic image issubjected to a smoothing filtering process. Additionally, when an imagedata compression is performed, another separation of image areas isperformed in response to degrees of the intensity slope in edge areas.

[0014] As mentioned above, in the conventional technique, two separationprocesses are performed on the same image data and the filtering processis performed separately from the quantizing process. Thus, there is aproblem in that a process time is increased and a hardware cost isincreased. Additionally, there is a disadvantage in the techniquedisclosed in the above-mentioned patent document in that a compressionrate is not minimized since a result of the area separation must be alsostored as the compressed data.

[0015] An image data compression technique is generally used in theimage data processing field so as to reduce a capacity of a memory forstoring image data or reduce a time for transmitting image data., Thereare various image data compressing methods depending on the processingmodes of image data. When image data is printed, a rotation of the imagemay be requested. In order to rotate the image at a high speed, a fixedlength compression is used.

[0016] Additionally, when image data is exchanged between systems havingdifferent resolutions or gradation characteristics, a compressing methodusing a layered data structure is desired so as to select transmissiondata corresponding to an image quality of an image outputting system.Especially, when image data is transmitted to a display apparatus, aprogressive transmission method is required. In the progressivetransmission method, image data of an object such as an icon can betransmitted prior to sending the image data. Thus, data compression isperformed in response to the level of layers.

[0017] Additionally, when a trial printing is performed for checking alayout while reducing toner consumption in an image printing apparatus,a data compressing method is required by which a feature of the image ismaintained but image quality is not reduced.

[0018] Japanese Laid-Open Patent Application No. 1-135265 discloses adata compressing method in which an original image is divided into aplurality of blocks, and each block is divided into image data which isorthogonal-transformed and other data so that a representative image ofthe image file can be effectively regenerated.

[0019] Generally, an image comprises an image area and an edge area. Inthe image area, a gradation of the image gradually changes, such as in aphotograph or a graphic image. In the edge area, a gradation sharplychanges in an area of an edge of the image and an area adjacent to theedge, such as in a character image or a line image. When the visualsense of human beings is considered, gradation is important in the imagearea whereas resolution is important in the edge area.

[0020] In the conventional technique disclosed in the above-mentionedpatent document, a sampling is performed on the original image, and thesampled image data is subjected to the discrete cosine transform (DCT).The same transform factor which is obtained from a quantization table isused for all areas. In such a case, a length of data is fixed since asingle quantization table is used. However, there is a drawback in thatthe quantized image data does not accurately represent the feature ofthe image while a large amount of data is used since a singlequantization is used. Additionally, there is a disadvantage in that onlytwo levels of image data can be selected.

SUMMARY OF THE INVENTION

[0021] It is a general object of the present invention to provide animproved and useful image processing system in which the above-mentionedproblems are eliminated.

[0022] A more specific object of the present invention is to provide animage processing system which compresses an image including both abinary image and a natural image by a sub-band transform method with ahigh compression rate.

[0023] Another object of the present invention is to provide an imageprocessing system which facilitates processing and editing of image databy using a sub-band transform method or a fixed length encoding methodat a low cost.

[0024] A further object of the present invention is to provide an imageprocessing system which can represent a feature of an image while areduced amount of data is used, and which can produce image dataincluding a plurality of levels of image quality.

[0025] In order to achieve the above-mentioned objects, there isprovided according to one aspect of the present invention an imageprocessing system comprising:

[0026] a buffer unit extracting n×m pixel matrix block data from imagedata, where n and m are integers;

[0027] a sub-band transform unit transforming the n×m pixel matrix blockdata by a sub-band transform method so as to obtain a transform factorhaving a plurality of frequency components; and

[0028] a quantizing unit quantizing the transform factor by afixed-length quantizing method by deleting a predetermined number oflower order bits of each of the frequency components.

[0029] According to the above-mentioned invention, since thehigh-frequency component of the sub-band transform factor is quantizedby deleting the lower order bits in the fixed-length quantizing method,various subsequent processes such as editing of the image or a rotationof the image can be easily performed with a reduced amount of compresseddata.

[0030] Additionally, there is provided according to another aspect ofthe present invention an image processing system comprising:

[0031] a buffer unit extracting n×m pixel matrix block data from imagedata, where n and m are integers;

[0032] a sub-band transform unit transforming the n×m pixel matrix blockdata by a sub-band transform method so as to obtain a transform factorhaving a plurality of frequency components including a low-frequencycomponent and a high-frequency component;

[0033] an area discriminating unit discriminating a type of an imagearea corresponding to the n×m pixel matrix block data being processed sothat the image area is determined as one of an edge area and a non-edgearea, a discrimination being made based on whether or not an absolutevalue of each of the components of the transform factor exceeds athreshold value; and

[0034] a quantizing unit quantizing the transform factor by afixed-length quantizing method by deleting a predetermined number oflower order bits of each of the frequency components, the number ofdeleted lower order bits of each of the frequency components of thetransform factor being changed in accordance with a type of image areabeing processed so that image data including the transform factor andflag information indicating a type of image area has a predeterminedfixed length.

[0035] According to this invention, a number of the lower order bits ofeach of the frequency components of the transform factor is deleted inresponse to a type of the image, and the transform factor is quantizedby a fixed-length quantizing method together with the flag information.Thus, the image data can be efficiently compressed with a high qualityirrespective of whether the image data corresponds to an edge area or anon-edge area. Additionally, the original image data can be, easilyrestored on a decoder side based on the flag information.

[0036] In the above-mentioned invention, the quantizing unit may deletelower order bits of each of the low-frequency component and thehigh-frequency component so that a number of deleted lower order bitsfor the edge area is greater than a number of deleted lower order bitsfor the non-edge area. Accordingly, data corresponding to both the edgearea in which a gradation is important and the non edge area in whichrecognition of an edge is important can be efficiently compressed whilea high image quality is maintained. Additionally, the original imagedata can be easily restored on a decoder side based on the flaginformation.

[0037] Additionally, the quantizing unit may quantize the high-frequencycomponent by a vector quantizing method. Further, the quantizing unitmay embed the flag information into the transform factor.

[0038] Additionally, the quantizing unit may change bit datarepresenting the transform factor so that the flag information isrepresented by a part of the data bits representing the transformfactor. The quantizing unit may change the transform factor so thatcorrelation between the transform factors of different types isincreased. The quantizing unit may change a bit arrangement of thetransform factor so that correlation between the transform factorcorresponding to the edge area and the transform factor corresponding tothe non-edge area is increased.

[0039] Additionally, there is provided according to another aspect ofthe present invention an image processing system comprising:

[0040] a buffer unit extracting n×m pixel matrix block data from imagedata, where n and m are integers;

[0041] a binarizing unit transforming the n×m pixel matrix block datainto binary data represented by a maximum value and a minimum value;

[0042] a differential data calculating unit calculating differentialdata which is a difference between a value of each pixel in the n×mpixel matrix block data and one of the maximum value and the minimumvalue of the binary data;

[0043] a sub-band transform unit transforming the differential data by asub-band transform method so as to obtain a transform factor having aplurality of frequency components; and

[0044] an encoding unit encoding the binary data and the sub-bandtransform factor so as to obtain a code representing the image data.

[0045] According to the above-mentioned invention, a continuous toneimage data can be efficiently compressed by a sub-band transform sincethe original image data is represented by using the binary data and thesub-band transform factor which are encoded by a sub-band transform.Accordingly, an image including a binary image and a continuous toneimage can be processed by a single method irrespective of types of theimage.

[0046] In the above-mentioned invention, the encoding unit may deletelower order bits of the sub-band transform factor so that the code has apredetermined fixed length. Accordingly, the image data including thebinary image data and the continuous tone image data can be compressedwith a high compression rate while a high image quality is maintained.The compression rate is higher than that of the block truncationencoding method.

[0047] Additionally, the encoding unit may delete a greater number oflower order bits from the high-frequency component than thelow-frequency component when both the maximum value and the minimumvalue exist in the binary data of the same block data. Accordingly, achange in an average intensity in a block having a sharp gradationchange can be prevented, resulting in a prevention of deterioration ofthe image quality.

[0048] Further, the encoding unit may quantize the high-frequencycomponent of the sub-band transform factor by a vector quantizingmethod.

[0049] Additionally, there is provided according to another aspect ofthe present invention an image processing system comprising:

[0050] a dividing unit dividing image data into a plurality of n×m pixelmatrix block data, where n and m are integers;

[0051] a transform unit transforming each pixel in the n×m pixel matrixblock data by a frequency transform method so as to produce a transformfactor including a high-frequency component and a low-frequencycomponent;

[0052] an image area discriminating unit for determining whether theblock being processed corresponds to an edge area or a non-edge areabased on the transform factor output from the transform unit;

[0053] a quantizing unit quantizing the transform factor for the edgearea and the transform factor for the non-edge area by differentmethods; and

[0054] an encoding unit encoding an output of the quantizing unit by anentropy encoding method,

[0055] wherein a total of a number of bits of the high-frequencycomponent and a number of bits of the low-frequency is the sameregardless of types of the edge area or the non-edge area, and a numberof bits of the high-frequency component for the edge area is the same asa number of bits of the low-frequency component of the non-edge area.

[0056] According to the above-mentioned embodiment, image datarepresenting a feature of the original image can be produced while anamount of data is reduced. Additionally, image data corresponding to aplurality of image quality levels can be produced.

[0057] In the above-mentioned invention, the encoding unit may alsoencode error data generated by the quantizing unit. Accordingly, therestored image data can almost completely match the original data.

[0058] Additionally, an encoding of the image for the edge area may beperformed by using only the high-frequency component, and an encoding ofthe image for the non-edge area is performed by using only thelow-frequency component. According to this invention, the image qualityof the restored image may be low, but a feature of the original imagecan be sufficiently maintained.

[0059] Further, in the above-mentioned invention, every other block datamay be used for restoring an original image. According to thisinvention, the image quality of the restored image may be low, but afeature of the original image can be sufficiently maintained while anamount of data is reduced. Thus, a reduced-size image can be easilyobtained.

[0060] Other objects, features and advantages of the present inventionwill become more apparent from the following detailed descriptions whenread in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0061]FIG. 1 is a block diagram of an image processing system accordingto a first embodiment of the present invention;

[0062]FIG. 2 is an illustration for explaining a sub-band transformationunit shown in FIG. 1;

[0063]FIG. 3 is an illustration for explaining a compressing andexpanding operation performed by the image processing system shown inFIG. 1;

[0064]FIG. 4A is an illustration for explaining a compressing operationperformed by the image processing system shown in FIG. 1 when a completebinary image fluctuates;

[0065]FIG. 4B is an illustration for explaining an expanding operationfor data obtained by the compressing operation of FIG. 4A;

[0066]FIG. 5 is an illustration for explaining an operation performed bythe image processing system shown in FIG. 1 for deleting lower orderbits;

[0067]FIG. 6 is an illustration for explaining an operation performed bythe image processing system shown in FIG. 1 for deleting lower orderbits;

[0068]FIG. 7 is an illustration of a quantization table;

[0069]FIG. 8 is a block diagram of an image processing system accordingto a second embodiment of the present invention;

[0070]FIG. 9 is an illustration for explaining a transformation of imagedata performed by the image processing system shown in FIG. 8

[0071]FIG. 10 is a block diagram of an image processing system accordingto a third embodiment of the present invention;

[0072]FIG. 11 is an illustration for explaining a compressing andexpanding operation performed by the image processing system shown inFIG. 10;

[0073]FIG. 12 is a block diagram of an image processing system accordingto a fourth embodiment of the present invention;

[0074]FIG. 13 is an illustration for explaining an operation of theimage processing system shown in FIG. 12;

[0075]FIG. 14A is an illustration for explaining a compressing andexpanding operation for a non-edge area performed by the imageprocessing system shown in FIG. 12;

[0076]FIG. 14B is an illustration for explaining a compressing andexpanding operation for an edge area performed by the image processingsystem shown in FIG. 12;

[0077]FIG. 15 is a block diagram of an image processing system accordingto a fifth embodiment of the present invention;

[0078]FIG. 16 is an illustration for explaining an operation of theimage processing system shown in FIG. 15;

[0079]FIG. 17A is an illustration for explaining a bit assignment for anedge area in the fifth embodiment;

[0080]FIG. 17B is an illustration for explaining a bit assignment for anon-edge area in the fifth embodiment;

[0081]FIG. 18A is an illustration for explaining a compressing andexpanding operation for a non-edge area performed by the imageprocessing system according to the fifth embodiment of the presentinvention:;

[0082]FIG. 18B is an illustration for explaining a compressing andexpanding operation for an edge area performed by the image processingsystem according to the fifth embodiment of the present invention;

[0083]FIG. 19 is a block diagram of an image processing system accordingto a sixth embodiment of the present invention;

[0084]FIG. 20 is an illustration for explaining a compressing andexpanding operation performed by the image processing system shown inFIG. 19;

[0085]FIG. 21 is an illustration for explaining an operation performedby an image processing system according to a seventh embodiment of thepresent invention;

[0086]FIG. 22A is an illustration for explaining a bit assignment for anon-edge area of the seventh embodiment;

[0087]FIG. 22B is an illustration for explaining a bit assignment for anedge area of the seventh embodiment;

[0088]FIG. 22C is an illustration for explaining discrimination of thenon-edge area and the edge-area;

[0089]FIG. 22D is an illustration of a vector quantization table used inthe seventh embodiment;

[0090]FIG. 23A is an illustration for explaining a compressing operationperformed in the seventh embodiment;

[0091]FIG. 23B is an illustration for explaining an enlarging operationperformed in the seventh embodiment; and

[0092]FIG. 24A is an illustration for explaining a bit assignment for anedge area of the seventh embodiment;

[0093]FIG. 24B is an illustration for explaining a bit assignment for anon-edge of the seventh embodiment;

[0094]FIG. 24C is an illustration for explaining a bit assignment for anedge area of an eighth embodiment;

[0095]FIG. 24D is an illustration for explaining a bit assignment for anon-edge of the eighth embodiment.

[0096]FIG. 25 is a block diagram of an image processing system accordingto a ninth embodiment of the present invention;

[0097]FIG. 26 is a circuit diagram of a wavelet transform unit shown inFIG. 25;

[0098]FIG. 27 is an illustration for explaining a pixel block and afrequency transform factor;

[0099]FIG. 28 is a graph showing a quantization characteristic;

[0100]FIG. 29A is an illustration for explaining quantizationrepresentative values and ranges defined by threshold values when a gainof a quantizing unit is 2.0;

[0101]FIG. 29B is an illustration for explaining quantizationrepresentative values and ranges defined by threshold values when a gainof a quantizing unit is 1.0;

[0102]FIG. 29C is an illustration for explaining quantizationrepresentative values and ranges defined by threshold values when a gainof a quantizing unit is 0.5;

[0103]FIG. 30A is an illustration of high-frequency components for anedge area;

[0104]FIG. 30B is an illustration of high-frequency components for anon-edge area;

[0105]FIG. 30C is an illustration of high-frequency components for anedge area in which two high-frequency components have large values;

[0106]FIG. 31 is a block diagram of an image processing system accordingto a tenth embodiment of the present invention;

[0107]FIG. 32A is an illustration showing an example of atwo-dimensional vector quantization using 7 quantization values;

[0108]FIG. 32B is an illustration showing an example of atwo-dimensional vector quantization using 15 quantization values;

[0109]FIG. 33 is an illustration for explaining an example of a bitarrangement for each area; and

[0110]FIG. 34 is an illustration of a part of an original image.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0111] A description will now be given, with reference to FIGS. 1through 6, of a first embodiment of the present invention. FIG. 1 is ablock diagram of an image processing system according to the firstembodiment of the present invention. FIG. 2 is an illustration forexplaining a sub-band transformation unit shown in FIG. 1. FIG. 3 is anillustration for explaining a compressing and expanding operationperformed by the image processing system shown in FIG. 1. FIGS. 4A and4B are illustrations for explaining a compressing and expandingoperation performed by the image processing system shown in FIG. 1 whena complete binary image fluctuates. FIGS. 5 and 6 are illustrations forexplaining an operation performed by the image processing system shownin FIG. 1 for deleting lower order bits.

[0112] The image processing system according to the first embodiment isapplied to a printer using 8-bit data (256 gradation levels) forrepresenting image data. The image processing system shown in FIG. 1comprises a buffer unit 201, a binary image differential data producingunit 202, a sub-band transformation unit 203, entropy-encoding units 204a and 204 b and a memory 205.

[0113] The buffer unit 201 extracts 2×2 matrix pixels from image data,and stores the 2×2 matrix pixel data therein. The 2×2 matrix pixel datastored in the buffer unit 201 is transformed into binary image data anddifferential data by the binary image differential data producing unit202. The binary data in the present embodiment is obtained bytransforming the image data having intensity values from “0” through“255” into one of binary values “255” and “0”. That is, the image datahaving an intensity value equal to or greater than “128” is transformedinto the value “255” and the image data having an intensity valuesmaller than “128” is transformed into the value “0”. It should be notedthat the binary values “255” and “0” are represented by values “1” and“0” respectively.

[0114] The differential data represents an absolute value of adifference between the original image data and one of the binary values“255” and “0”. The data having an intensity value equal to or greaterthan “128” is calculated by the following equation.

(differential data)=(binary data “255”)−(intensity value)

[0115] The data having an intensity value smaller than “128” iscalculated by the following equation.

(differential data)=(intensity value)−(binary data “0”)

[0116] When a reverse transformation is performed, the data having anintensity value equal to or greater than “128” is calculated by thefollowing equation.

(reverse transform value)=(binary data “255”)−(differential value)

[0117] The data having an intensity value smaller than “128” iscalculated by the following equation.

(reverse transform value)=(differential value)−(binary data “0”)

[0118] The binary data “255” and “0” are then encoded by using anentropy encoding by the entropy-encoding unit 104 a. The encoded data isstored in the memory 205. On the other hand, the differential data istransformed by using a sub-band transform method such as the HarrWavelet transform method by the sub-band transform unit 203. Then, thetransformed data is encoded by using the entropy-encoding by the entropyencoding unit 204 b. The encoded differential data is also stored in thememory 205.

[0119] In this embodiment, the sub-band transform unit 203 transformsthe differential data of pixels a through d shown in FIG. 2 by using theHarr Wavelet transform method so as to obtain a low frequency componentLL and high-frequency components HL, LH and HH as shown in FIG. 2. Atthis time, decimals are omitted.

LL={(a+b)/2+(c+d)/2}/2

HL={(a−b)+(c−d)}/2

LH={(a+b)−(c+d)}/2

HH=(a−b)−(c−d)  (1)

[0120] The differential data between the original image data and one ofthe binary data “255” and “0” takes a value from “0” to “127”. The LLcomponent is represented by 7-bit data since it takes a value from “0”to “127”. Each of the HL component and the LH component is representedby 8-bit, data since it takes a value from “−127” to “127”. The HHcomponent is represented by 9-bit data since it takes a value from“−255” to “255”. When the reverse sub-band transform is performed, theoriginal image data of the pixels a through d are restored by using theLL, HL, LH, HH components and the above equations (1).

[0121] A description will now be given, with reference to FIG. 3, of anexample of a process performed by the image processing system shown inFIG. 1. It is assumed that original image data 301 including the pixelsa through d is input to the image processing system, and the pixels athrough d have the following intensity values.

[0122] a=175

[0123] b=0

[0124] c=175

[0125] d=20

[0126] In this case, the binary data of the pixels a through d arerepresented as follows.

[0127] a=255=“1”

[0128] b=0=“0”

[0129] c=255=“1”

[0130] d=0=“0”

[0131] Additionally, the differential data 302 of the pixels a through dis represented as follows.

[0132] a=255−175=80

[0133] b=0−0=0

[0134] c=255−175=80

[0135] d=20−0=20

[0136] The differential data 302 is transformed by the sub-bandtransform method, and the following data 303 is obtained.

[0137] LL=45

[0138] HL=70

[0139] LH=−10

[0140] HH=20

[0141] The data 303 is temporarily stored in a memory 304. The data 303is read to obtain the original differential data 302 by performing areverse sub-band transform based on the equations (1). Additionally, thedifferential data 302 can be calculated based on the binary data “0” and“1”.

[0142] a=255−80=175

[0143] b=0−0=0

[0144] c=255−80=175

[0145] d=20−0=20

[0146] Accordingly, the original data is restored.

[0147] When a continuous tone image is compressed, an amount ofinformation of binary data after compression or an amount of informationof sub-band transform factors after compression is negligibly small.This is because the binary data of the continuous tone image is a simplebinary image, and it can be compressed into very small amount.Additionally, the differential data (sub-band transform factors aftercompression) is close to “0”.

[0148] A description will now be given of a case of a digital copymachine in which image data corresponding to a single page is compressedbased on a fixed-length compression method, and is stored in a memory(hereinafter referred to as a page memory) for processing and editing(90-degree rotation) the image. With respect to a quantization, as shownin FIGS. 4A and 4B, the LL component is represented by a multiple of“4”: each of the HL and LH components is represented by a multiple of“16”; and the HH component is represented by a multiple of “64”. Thatis, when the quantization is performed, the LL component is divided by“4” (two lower order bits are deleted); each of the HL and LH componentsis divided by “16” (four lower order bits are omitted); and the HHcomponent is divided by “64” (six lower order bits are deleted).According to the above-mentioned quantization, the LL component (0 to255) can be represented by 6-bit data; each of the HL and LH components(−255 to 255) can be represented by 5-bit data; and the HH component(−510 to 510) can be represented by 4-bit data. The total number of thedata bits is 20 bits.

[0149] When the copy machine scans an original image, complete binaryimage data cannot be obtained due to fluctuation in an intensity of theimage, the complete binary image data comprising only the minimum values“0” and the maximum values “255”. FIGS. 4A and 4B shows a process forcompressing and expanding original image data 401 which is not thecomplete binary image data as follows.

[0150] a=250≠255

[0151] b=0

[0152] c=254≠255

[0153] d=2≠0

[0154] Binary data 402 is obtained based on the threshold value “128” asfollows.

[0155] a=255=“1”

[0156] b=0=“0”

[0157] c=255=“1”

[0158] =0=“0”

[0159] Additionally, differential data 403 becomes as follows.

[0160] a=255−250=5

[0161] b=0−0=0

[0162] c=255−254=1

[0163] d=2−0=2

[0164] The restored data 408 shown in FIG. 4B is complete binary imagedata since the differential data 402 is quantized after being subjectedto the sub-band transformation. That is, the quantization is performedso that the LL component of the differential data becomes 6-bit data,each of the HL and LH components of the differential data becomes 5-bitdata and the HH component of the differential data becomes 4-bit data.According to such a quantization method, a fixed length encoding can beachieved with a reduced amount of encoded information. Thus, the binaryimage data can be corrected to complete binary image data by eliminatinga fluctuation generated when the original image is scanned.Additionally, when the thus quantized data is encoded by an entropyencoding method, the-quantized data can be compressed at a highcompression rate since each of the sub-band transformation factors LL,HL, LH and HH is “0”. Thus, a compression rate, which is almost equal toa compression rate when an image is simply binarized, can be achieved.

[0165] A description will now be given, with reference to FIGS. 5 and 6,of a case in which an image having deterioration is compressed, thedeterioration of the image being peculiar to an image having a block inwhich gradation is sharply changed. For the sake of simplification, itis assumed that a one-dimensional transformation is used so as totransform original data 501 (x0, x1)=(96, 191) as follows.

[0166] (1) The original data 501 is binarized based on the thresholdvalue “128”→(0, 255);

[0167] (2) Differential data 502 between the original image data and oneof the binary data “255” and “0” is obtained.→96−0=96, 255−191=64

[0168] Then, the obtained differential data 502 is transformed by theHarr Wavelet transform method.→L=(96+64)/2=80, H=64−96=−32

[0169] (3) The data L and H are quantized (lower order bits aredeleted).

[0170] Since the low-frequency component L is more important than thehigh-frequency component H, the high-frequency component H is quantizedroughly (more number of lower order bits are deleted) while thelow-frequency component L is quantized finely (less number of lowerorder bits are deleted). If the low-frequency component L is notquantized and the high-frequency component H is quantized by a multipleof “64”, the low-frequency component L and the high-frequency componentH become as follows.

[0171] L=80, H=0

[0172] The thus obtained L and H are restored to the differential data503 by a reverse sub-band transformation. Then, the decoded data 504 isrestored from the differential data 503. That is, as shown in FIG. 5,the decoded data 504 is changed from (96, 191) to (80, 175). That is,the intensities of both pixel values in the decoded data 504 aredecreased from those of the original pixel values. This phenomenon isgenerated when a pixel which is rendered to be the value “255” and apixel which is rendered to be the value “0” are present in the samepixel block. This phenomenon is also generated in a two-dimensionalpixel block. Thus, there is a problem in that there is a considerabledifference between an original image and a restored image.

[0173] In order to eliminate the above-mentioned problem, in the presentinvention, when both a pixel rendered to be the value “255” and a pixelrendered to be the value “0” are present in the same pixel block, thehigh-frequency components are quantized finely (less number of lowerorder bits are deleted). Referring to FIG. 6, pixel data 601 (96, 191)is transformed into data 602 including binary data and differential data(96, 64). The differential data (96, 64) is transformed by the HarrWavelet transform method, and factors L=80 and H=−32 are obtained.

[0174] When the factors are quantized, the high-frequency component H isquantized finely, and the low-frequency component L is quantizedcoarsely. For example, when the high-frequency component H is quantizedby a multiple of 64, and the low-frequency component L is quantized by amultiple of 32, the sub-band factors becomes as follows.

[0175] L=64, H=−32

[0176] These factors are subjected to a reverse sub-band transformationso as to obtain differential data 603. Then, pixel data 604 is obtainedfrom the thus-obtained differential data 603. As shown in FIG. 6, thepixel data 604 is changed from the original data (96, 191) to (80, 207),but the sum of the two pixel values is not changed. Accordingly, arelative intensity of the pixels is not changed.

[0177] Generally, in a pixel block having two binary values, anintensity of an entire pixel block does not change whereas the intensityis changed when the high-frequency components are deteriorated.Accordingly, a change in color over an entire image can be prevented byfinely quantizing the high-frequency components H.

[0178] A description will now be given of a second embodiment of thepresent invention. In the second embodiment, the high-frequencycomponents are not sampled on each individual component basis but asampling operation is performed on a combination of the high-frequencycomponents so as to achieve an efficient sampling operation. An imageblock having the HL component representing a vertical edge and the LHcomponent representing a horizontal edge being a large value rarelyappears in an image. If an image block has one of the HL and the LHcomponents being a large value and the other one of the HL and LHcomponents being a small value and also the HH component being a smallvalue, such an image block corresponds to either a vertical line orhorizontal line in the image. Thus, such an image block frequentlyappears in an image. Additionally, if an image block has thehigh-frequency components HL, LH and HH all of which are small values,such an image block corresponds to an image area having a uniformintensity or an image area having a gentle change in intensity. Thus,such a block frequently, appears in an image. Accordingly, the imagedata can be efficiently quantized by appropriately assigning codes to acombination of the high-frequency components. This quantization isreferred to as a vector quantization.

[0179]FIG. 7 is a table showing a code assignment to variouscombinations of the high-frequency components HL, LH and HH whichfrequently appear in an image. Codes “0” through “15” are assigned tothe combinations of the high-frequency components HL, LH and HH.According to this method, each of the high-frequency components HL andLH can be compressed into 4-bit data. In the quantizing method, adifference P between the transformation factor and each value of thevector quantization is obtained for each individual component, and aquantization code which minimizes the difference P is used.

P=|HL−HLqi|+|LH−LHqi|+|HH−HHqi|

[0180] Where HLqi, LHqi and HHqi are quantization values correspondingto a code value i in the quantization table shown in FIG. 7.

[0181]FIG. 8 is a block diagram of an image processing system accordingto the second embodiment of the present invention. FIG. 9 is anillustration for explaining a transformation of image data performed bythe image processing system shown in FIG. 8 As shown in FIG. 8, theimage processing system according to the second embodiment of thepresent invention has the same structure as the image processing systemaccording to the first embodiment except for a low-frequency componentquantizing unit 206 and a high-frequency component quantizing unit 207being added between the sub-band transform unit 203 and the entropyencoding units 204 b and 204 c.

[0182] The 2×2 matrix pixel data extracted and stored in the buffer unit201 is transformed into binary image data and differential data by thebinary image differential data producing unit 202. Then, the binary datais encoded by the entropy encoding unit 204 a by using the entropyencoding method. The encoded data is stored in the memory 205. Thedifferential data is transformed by the sub-band transform unit 203 byusing the Harr Wavelet transformation method. The low-frequencycomponent LL of the transformed data is quantized by the low-frequencycomponent quantizing unit 206, and then the quantized data is encoded bythe entropy encoding unit 204 b. The encoded data is stored in thememory 205. The high-frequency components HL, LH and HH of thetransformed data are quantized by the high-frequency componentquantizing unit 207, and then the quantized data is encoded by theentropy encoding unit 204 c. The encoded data is stored in the memory205.

[0183] In FIG. 9, image data 901 which is the same as that shown in FIG.3 is compressed and enlarged.

[0184] a=175

[0185] b=0

[0186] c=175

[0187] d=20

[0188] The binary data 902 is obtained based on the threshold value“128” as follows.

[0189] a=255=“1”

[0190] b=0=“0”

[0191] c=255=“1”

[0192] d=0=“0”

[0193] The differential data 902 becomes as follows.

[0194] a=255−175=80

[0195] b=0−0=0

[0196] c=255−175=70

[0197] d=20−0=0

[0198] The differential data 902 is subjected to a sub-band transformusing the equation (1), and the following data 903 is obtained as aresult.

[0199] LL=45

[0200] HL=70

[0201] LH=−10

[0202] HH=20

[0203] The LL component is subjected to a linear quantization usingquantization representing values which are multiples of “4”, and thefollowing result is obtained.

[0204] LL=44

[0205] As for the high frequency components HL, LH and HH, the followingcombination which is closest to the combination (HL, LH, HH)=(70, −10,20) is selected from the quantizaiton table shown in FIG. 7.

[0206] (HL, LH, HH)=(64, 0, 0)

[0207] The corresponding code 5 in the quantization table is set to thevector quantization value H (H=5).

[0208] When an encoding operation is performed, differential data 908restored based on factors 907 which are (LL, HL, LH, HH)=(44, 64, 0, 0)becomes as follows.

[0209] a=76

[0210] b=12

[0211] c=76

[0212] d=12

[0213] Finally, image data 909 is obtained from the differential data908 based on the binary data a=255, b=0, c=255 and d=12 as follows.

[0214] a=255−76=179

[0215] b=12−0=12

[0216] c=155−76=179

[0217] d=12−0=12

[0218] As mentioned above, according to the second embodiment of thepresent invention, the high-frequency components can be represented by4-bit data in total since the high-frequency components are quantized bythe vector quantizing method. Additionally, the LL component becomes5-bit data by quantizing the original 7-bit data using a multiple of 4.Further, the binary image data can be represented by 4-bit data. Thus,the entire factor corresponding to the 2×2 pixel block can berepresented by 13-bit data. Thus, a fixed Length encoding method, whichis more efficient than a method which does not use the vectorquantization, can be achieved.

[0219] A description will now be given, with reference to FIGS. 10 and11, of a third embodiment of the present invention. FIG. 10 is a blockdiagram of an image processing system according to the third embodimentof the present invention. FIG. 11 is an illustration for explaining acompressing and expanding operation performed by the image processingsystem shown in FIG. 10. In FIG. 10, a sub-band transform unit 1202transforms the image data corresponding to the pixels a through dreceived from a 2×2 buffer unit 1201 by using the Harr Wavelet transformmethod so as to obtain the low-frequency component LL and thehigh-frequency components HL, LH and HH. At his time, decimals areomitted. When a reverse sub-band transform is performed, the originalimage data corresponding to the pixels a through d is restored based onthe components LL, HL, LH and HH and the equations (1).

[0220] The LL component takes a value from “0” to “255”, and isrepresented by 8-bit data. Each of the HL and LH components takes avalue from “−255” to “255”, and is represented by 9-bit data. The HHcomponent takes a value from “−510” to “510”, and is represented by10-bit data. Thus, the total number of bits is 36. The importance ofeach of the sub-band transformation factors LL, HL, LH and HH isdifferent, and a large part of lower order bits of the high-frequencycomponents HL, LH and HH can be deleted.

[0221] Accordingly, in this embodiment, a LL component quantizing unit1203 quantizes the LL component into a multiple of 4. An HL componentquantizing unit 1204 quantizes the HL component into a multiple of 16.An LH component quantizing unit 1205 quantizes the LH component into amultiple of 16. The HH component is rendered to be “0” that is the HHcomponent is discarded. Specifically, the LL component is transformedfrom 8-bit data to 6-bit data by being divided by 4. Each of the HL andLH components is transformed from 9-bit data to 5-bit data by beingdivided by 16. The HH component is transformed from 10-bit data to 0 bybeing discarded. Accordingly, the total number of bits of the factors ofthe 2×2 pixel block is reduced from 36 to 16. The quantized valuesobtained by the quantizing units are stored in a page memory 1206.

[0222] A description will now be given, with reference to FIG. 11, of aspecific operation of the image processing system shown in FIG. 10.

[0223] It is assumed that the following image data 1301 corresponding tothe pixels a through d shown in FIG. 2 is input to the sub-bandtransform unit 1202.

[0224] a=200

[0225] b=202

[0226] c=204

[0227] d=208

[0228] The image data is transformed by the Harr Wavelet transformmethod and the following factors 1302 are obtained.

[0229] LL=203

[0230] HL=−3

[0231] LH=−5

[0232] HH=2

[0233] Lower order bits of the factors 1302 are deleted, and thefollowing quantization data 1303 is obtained.

[0234] LL=50

[0235] HL=0

[0236] LH=0

[0237] HH=0

[0238] The following quantization data 1303 is stored in the page memory1206, and then two 0-bits are added so as to obtain the followingfactors 1304.

[0239] LL=200

[0240] HL=0

[0241] LH=0

[0242] HH=0

[0243] Thereafter, the factors 1304 are transformed by the reverse HarrWavelet transform method so as to restore the following image data 1305.

[0244] a=200

[0245] b=200

[0246] c=200

[0247] d=200

[0248] Accordingly, despite of the number of bits being greatly reduced,the restored image data 1305 is almost equal to the original image data1301. Additionally, the fixed length encoding using the sub-bandtransform can be performed by simple calculations such as addition andsubtraction in the equations (1) and a bit shift. Additionally, a goodimage quality can be obtained by the fixed-length encoding according tothe present embodiment.

[0249] A description will now be given, with reference to FIGS. 12 to14, of a fourth embodiment of the present invention. FIG. 12 is a blockdiagram of an image processing system according to the presentinvention. The image processing system according to the fourthembodiment of the present invention comprises the 2×2 buffer unit 1201,the sub-band transform unit 1202 and the page memory 1206. The imageprocessing unit shown in FIG. 12 further comprises an areadiscriminating unit 1403 and a sub-band transform factor quantizing andencoding unit 1404. The area discriminating unit 1403 classifies areasof the image into one of an edge area having a sharp gradation changeand a non-edge area other than the edge area based on the factors HL andLH which are transformed by the sub-band transform unit 1202.Specifically, the area discriminating unit 1403 determines that an areato be processed is an edge area if an absolute value of one of the HLand LH components is equal to or greater than a threshold value “64”.The area discriminating unit 1403 determines that the area to beprocessed is the non-edge area if an absolute value of one of the HL andLH components is less than the threshold value “64”. If the area to beprocessed is determined to be the edge area, the area discriminatingunit 1403 supplies a value “1” as a flag value to the sub-band transformfactor quantizing and encoding unit 1404 and the page memory 1206. Ifthe area to be processed is determined to be the non-edge area, the areadiscriminating unit 1403 supplies a value “0” as a flag value to thesub-band transform factor quantizing and encoding unit 1404 and the pagememory 1206.

[0250] The sub-band transform factor quantizing and encoding unit 1404quantizes the LL component of the edge area by a multiple of 4 (divideby 4) so as to change the LL component from 8-bit data to 6-bit data.Additionally, the sub-band transform factor quantizing and encoding unit1404 quantizes each of the HL and LH components by a multiple of 64(divide by 64) so as to change each of the HL and LH components from9-bit data to 3-bit data. With respect to the non-edge area, thesub-band transform factor quantizing and encoding unit 1404 quantizesthe LL component by a multiple of 4 (divide by 4) so as to change the LLcomponent from 8-bit data to 6-bit data. Additionally, the sub-bandtransform factor quantizing and encoding unit 1404 quantizes each of theHL and LH components by rendering an absolute value of each of the HLand LH components to become one of values 0, 16, 32 and 48 so as tochange the HL and LH components from 9-bit data to 3-bit data.Additionally, the HH component of either the edge area or the non-edgearea is discarded so as to change the HH component from 10-bit data to0.

[0251] A description will now be given, with reference to FIGS. 14A and14B, of a specific example of an operation of the image processingsystem shown in FIG. 12. FIG. 14A shows a case in which the followingimage data 1501 which is extracted from a non-edge area is processed.

[0252] a=200

[0253] b=202

[0254] c=204

[0255] d=208

[0256] The image data 1501 is subjected to the Harr Wavelet transform,and the following factors 1502 are obtained.

[0257] LL=203

[0258] HL=−3

[0259] LH=−5

[0260] HH=2

[0261] Since absolute values of both the HL and LH components are lessthan the threshold value “64”, the pixel block is determined as thenon-edge area. Accordingly, the LL component is quantized by a multipleof 4 and the HL and LH components are divided by 16 and the HH componentis discarded, which results as follows LL = 50 (6 bits) HL = 0 (3 bits)LH = 0 (3 bits) HH = 0 (0 bit) Flag = 0 (1 bit) Total = 13 bits

[0262] The quantized data 1503 is stored in the page memory 1206, andthen 0-bits are added to obtain the following factors 1504. The numberof added 0-bits corresponds to the number of the previously deletedbits.

[0263] LL=200

[0264] HL=0

[0265] LH=0

[0266] HH=0

[0267] Thereafter, the factors 1504 are subjected to the reverse HarrWavelet transform so as to obtain decoded image data 1505 as follows.

[0268] a=200

[0269] b=200

[0270] c=200

[0271] d=200

[0272] It should be appreciated that the decoded image data is roughlyequal to the original image data 1501.

[0273]FIG. 14B shows a case in which the following image data 1511 thatis extracted from an edge area is processed.

[0274] a=20

[0275] b=30

[0276] c=120

[0277] d=150

[0278] The image data 1511 is subjected to the Harr Wavelet transform,and the following factors 1512 are obtained.

[0279] LL=80

[0280] HL=−20

[0281] LH=−110

[0282] HH=20

[0283] Since the absolute value of the LH component is not less than thethreshold value “64”, the pixel block is determined as the edge area.Accordingly, the LL component is quantized by a multiple of 4 and the HLand LH components are quantized by a multiple of 64 and the HH componentis discarded, which produces results as follows. LL = 20 (6 bits) HL = 0(3 bits) LH = −1 (3 bits) HH = 0 (0 bit) Flag = 1 (1 bit) Total = 13bits

[0284] The quantized data 1513 is stored in the page memory 1206, andthen 0-bits are added to obtain the following factors 1514. The numberof the added 0-bits corresponds to the number of the previously deletedbits.

[0285] LL=80

[0286] HL=0

[0287] LH=−64

[0288] HH=0

[0289] Thereafter, the factors 1514 are subjected to the Harr Wavelettransform so as to obtain decoded image data 1515 as follows.

[0290] a=24

[0291] b=24

[0292] c=112

[0293] d=112

[0294] It should be appreciated that the decoded image data 1515 stillrepresents an edge area despite of a large number of bits being deleted.

[0295] In the third embodiment, the HL and LH components are quantizedby a multiple of 16 irrespective of whether the pixel block to beprocessed corresponds to the edge area or the non-edge area. However, inthe fourth embodiment, the HL and LH components are quantized by amultiple of 64 when the absolute values of the HL and LH components areequal to or greater than the threshold value “64”. Thus, thequantization representative values such as “96” or “106” are eliminated.Thus, the number of bits of the quantized data in the fourth embodimentis less than that of the third embodiment. However, in the edge area inwhich a gradation change is sharp, recognizable deterioration in theimage is rarely generated when the values of the high-frequencycomponents are coarsely sampled. Thus, the information regarding thehigh-frequency components can be deleted while the image quality ismaintained at a certain level. Thus, the fixed-length encoding can beachieved by the above-mentioned separation of an image area.

[0296] A description will now be given, with reference to FIGS. 15 to18, of a fifth embodiment of the present invention. The fifth embodimenthas the same structure as that of the fourth embodiment except for thearea discriminating unit 1403 and the sub-band transform factorquantization an encoding unit 1404 being replaced by an areadiscriminating unit 1403 a and a sub-band transform factor quantizingand encoding unit 1404 a. The area discriminating area 1403 a classifiesareas of the image into one of an edge area having a sharp gradationchange and a non-edge area other than the edge area based on the factorsHL and LH which are transformed by the sub-band transform unit 1202.Specifically, the area discriminating unit 1403 a determines that anarea to be processed is an edge area if an absolute value of one of theHL and LH components is equal to or greater than a threshold value “16”.Otherwise, the area discriminating unit 1403 a determines that the areato be processed is the non-edge area if an absolute value of one of theHL and LH components is less than the threshold value “16”. If the areato be processed is determined to be the edge area, the areadiscriminating unit 1403 a supplies a value “1” as a flag value to thesub-band transform factor quantizing and encoding unit 1404 a and thepage memory 1206. If the area to be processed is determined to be thenon-edge area, the area discriminating unit 1403 a supplies a value “0”as a flag value to the sub-band transform factor quantizing and encodingunit 1404 and the page memory 1206.

[0297] The sub-band transform factor quantizing and encoding unit 1404 achanges a bit assign ratio used by a fixed-length encoding method inresponse to the determination as to whether the image area to beprocessed is the edge area or the non-edge area. For example, for thenon-edge area, 6 bits are assigned to the LL component since a gradationis important for visual sense, whereas 3 bits are assigned to each ofthe HL and LH components. On the other hand, for the edge-area, 4 bitsare assigned to the LL component since recognition of an edge isimportant for visual sense, and 4 bits are assigned to each of the HLand LH components.

[0298] That is, for the edge-area, the sub-band transform factorquantizing and encoding unit 1404 a quantizes the LL component of theedge area by a multiple of 16 (divide by 16) so as to change the LLcomponent from 8-bit data to 4-bit data. Additionally, the sub-bandtransform factor quantizing and encoding unit 1404 a quantizes each ofthe HL and LH components by a multiple of 32 (divide by 32) so as tochange each of the HL and LH components from 9-bit data to 4-bit data.With respect to the non-edge area, the sub-band transform factorquantizing and encoding unit 1404 a quantizes the LL component by amultiple of 4 (divide by 4) so as to change the LL component from 8-bitdata to 6-bit data. Additionally, the sub-band transform factorquantizing and encoding unit 1404 a quantizes each of the HL and LHcomponents by a multiple of 4 (divide by 4) so as to change the HL andLH components from 9-bit data to 3-bit data. Additionally, the HHcomponent of either the edge area or the non-edge area is discarded soas to change the HH component from 10-bit data to 0.

[0299] A description will now be given, with reference to FIGS. 18A and18B, of a specific example of an operation of the image processingsystem according to the present embodiment. FIG. 18A shows a case inwhich the following image data 1801 which is extracted from a non-edgearea is processed.

[0300] a=200

[0301] b=202

[0302] c=204

[0303] d=208

[0304] The image data 1801 is subjected to the Harr Wavelet transform,and the following factors 1802 are obtained.

[0305] LL=203

[0306] HL=−3

[0307] LH=−5

[0308] HH=2

[0309] Since absolute values of both the HL and LH components are lessthan the threshold value “16”, the pixel block is determined as thenon-edge area. Accordingly, the LL component is quantized by a multipleof 4, the HL and LH components are quantized by a multiple of 4 and theHH component is discarded, which produces results as follows.

[0310] LL=50 (6 bits)

[0311] HL=0 (3 bits)

[0312] LH=0 (3 bits)

[0313] HH=0 (0 bit)

[0314] Flag=0 (1 bit)

[0315] Total=13 bits

[0316] The quantized data 1803 is stored in the page memory 1206, andthen 0-bits are added to obtain the following factors 1804. The numberof added 0-bits corresponds to the number of the previously deletedbits.

[0317] LL=200

[0318] HL=0

[0319] LH=−4

[0320] HH=0

[0321] Thereafter, the factors 1804 are subjected to the reverse HarrWavelet transform so as to obtain decoded image data 1805 as follows.

[0322] a=198

[0323] b=198

[0324] c=202

[0325] d=202

[0326]FIG. 18B shows a case in which the following image data 1811 whichis extracted from an edge area is processed.

[0327] a=20

[0328] b=30

[0329] c=120

[0330] d=150

[0331] The image data 1811 is subjected to the Harr Wavelet transform,and the following factors 1812 are obtained.

[0332] LL=80

[0333] HL=−

[0334]20

[0335] LH=−110

[0336] HH=20

[0337] Since the absolute value of the LH component is not less than thethreshold value “16”, the pixel block is determined as the edge area.Accordingly, the LL component is quantized by a multiple of 4, the HLand LH components are quantized by a multiple of 32 and the HH componentis discarded, which results as follows LL = 5 (4 bits) HL = 0 (4 bits)LH = −3 (4 bits) HH = 0 (0 bit) Flag = 1 (1 bit) Total = 13 bits

[0338] The quantized data 1813 is stored in the page memory 1206, andthen 0-bits are added to obtain the following factors 1814. The numberof the added 0-bits corresponds to the number of the previously deletedbits.

[0339] LL=80

[0340] HL=0

[0341] LH=−96

[0342] HH=0

[0343] Thereafter, the factors 1514 are subjected to the reverse HarrWavelet transform so as to obtain decoded image data 1515 as follows.

[0344] a=32

[0345] b=32

[0346] c=128

[0347] d=128

[0348] In the third embodiment, the HL and LH components are quantizedby a multiple of 16 irrespective of whether the pixel block to beprocessed corresponds to the edge area or the non-edge area. However, inthe fifth embodiment, the factors are encoded by using different numbersof assigned bits in response to the determination as to whether theimage area corresponds to the edge area or the non-edge area. That is,the LL component of the non-edge area in which gradation is important isfinely sampled, whereas the HL and LH components of the edge area inwhich a change in intensity is important are finely sampled. Thus, thefixed-length encoding can be achieved while the feature of an image ismaintained.

[0349] A description will now be given, with reference to FIGS. 19 and20, of a sixth embodiment of the present invention. FIG. 19 is a blockdiagram of an image processing system according to the sixth embodimentof the present invention. FIG. 20 is an illustration for explaining acompressing and expanding operation performed by the image processingsystem shown in FIG. 19.

[0350] In the present embodiment, pixel values a through d are subjectedto the Harr Wavelet transform by the sub-band transform unit 1202 asshown in FIG. 12. Then, the low-frequency component LL is quantized bythe low-frequency quantizing unit 206 shown in FIG. 8, and thehigh-frequency components HL, LH and HH are vector-quantized by thevector quantizing unit 207 based on the quantization table shown in FIG.7.

[0351] Referring to FIG. 20, the following original image data 1101 isinput to the sub-band transform unit 1202.

[0352] a=20

[0353] b=30

[0354] c=120

[0355] d=150

[0356] The original image data 1101 is subjected to the Harr Wavelettransform, and the following factors 1102 are obtained. LL = 80 (8 bits)HL = −20 (9 bits) LH = −110 (9 bits) HH = 20 (10 bits) Total 36 bits

[0357] The LL component is represented by 6-bit data as follows.

[0358] LL=20 (6 bits)

[0359] The HL, LH and HH components (−20, −110, 20) are selected fromthe quantization table shown in FIG. 7. That is, the following valueswhich are closest to the combination of the values (−20, −110, 20) isselected.

[0360] (HL, LH, HH)=(0, −128, 0)

[0361] Then, the corresponding code “12” (4 bits) which represents thequantization representative vector is set as the quantization value.Thus, the original image data 1101 is compressed to the data 1103 having10 bits as the total number of bits.

[0362] In a decoding process, two 0-bits are added to the LL component(LL=20 (6 bits)). Then, the quantized data 1103 is decoded to the data1104 based on the vector quantization value “12” as follows.

[0363] (LL, HL, LH, HH)=(80, 0, −128, 0) Thereafter, the data 1104 issubject to the reverse Harr Wavelet transform, and the following imagedata 1105 is obtained.

[0364] a=16

[0365] b=16

[0366] c=144

[0367] d=144

[0368] A description will now be given, with reference to FIGS. 21 to23, of a seventh embodiment of the present invention. In the seventhembodiment, the bit assignment of the fifth embodiment and the vectorquantization of the high-frequency components are combined.

[0369] In the present embodiment, similar to the fifth embodiment, it isdetermined that an area to be processed is an edge area if an absolutevalue of one of the HL and LH components is equal to or greater than athreshold value “16”. On the other hand, it is determined that the areato be processed is the non-edge area if an absolute value of one of theHL and LH components is less than the threshold value “16”. If the areato be processed is determined to be the edge area, four higher orderbits are assigned to the low-frequency component LL, and four lowerorder bits are assigned to the high frequency components HL, LH and HHas shown in FIG. 22A. On the other hand, if the area to be processed isthe non-edge area, six higher order bits are assigned to thelow-frequency component LL and two lower order bits are assigned to thehigh-frequency components HL, LH and HH as shown in FIG. 22B.Additionally, the two lower order bits are used to indicate flaginformation as shown in FIG. 22C. That is, the two lower order bits areset to “00” for the non-edge area, and “00” is not set to the two lowerorder bits for the edge area.

[0370] With respect to quantization, as shown in FIG. 22D, the LLcomponent of the edge area is divided by 16 so as to obtain 4-bit data,and the high-frequency components are vector-quantized to be representedby 4-bit data. It should be noted that values 0=0000, 4=0100, 8=1000 and12=1100 are not used since these values have the two lower order bits“00” which is reserved for the flag information. Additionally, the LLcomponent of the non-edge area is divided by 4 so as to obtain 6-bitdata, and only “00” is used for the high-frequency components.

[0371]FIG. 23A is an illustration for explaining a compressing operationperformed in the seventh embodiment, and FIG. 23B is an illustration forexplaining an enlarging operation performed in the seventh embodiment.

[0372] It is assumed that the following image data 1301 which isextracted from the edge area is input.

[0373] a=20

[0374] b=30

[0375] c=120

[0376] d=150

[0377] The image data 1301 is subjected to the Harr Wavelet transform,and the following factors 1302 are obtained

[0378] LL=80

[0379] HL=−20

[0380] LH=−110

[0381] HH=20

[0382] Since absolute values of both the HL and LH components aregreater than the threshold value “16”, the block being processed isdetermined as the edge area. Thus, the LL component is divided by 16 andthe result is as follows.

[0383] LL=20=1010 (4 bits)

[0384] With respect to the high-frequency components, the followingcombination which is closest to the HL, LH and HH components (−20, −110,20) is selected.

[0385] (HL, LH, HH)=(0, −128, 0)

[0386] Additionally, the code “13=0111 (4 bits)” representing thequantization representative vector is set to the quantization value1303. Thus, the quantized data 1304 having a total number of bits being8 bits is obtained. The quantized data 1304 is stored in the page memory1206 as data 1305.

[0387] When encoding is performed, the data 1305 being processes is readfrom the page memory 1206 as data 1306. The data 1306 is determined asthe non-edge area since the two lower order bits are not “00”. Thus, thefollowing factors 1308 are obtained.

[0388] LL=80

[0389] HL=0

[0390] LH=−128

[0391] HH=0

[0392] Thereafter, the factors 1308 are subject to the reverse HarrWavelet transform, and the following decoded image data 1309 isobtained.

[0393] a=16

[0394] b=16

[0395] c=144

[0396] d=144

[0397] As mentioned above, according to the seventh embodiment, sincethere is no need to assign bits for indicating the flag information, theimage data can be efficiently encoded when the number of bits used bythe fixed length encoding method is limited.

[0398] A description will now be given, with reference to FIGS. 24A,24B, 24C and 24D, of an eighth embodiment of the present invention.

[0399]FIG. 24A is an illustration of 8-bit data corresponding to theedge area obtained in the above-mentioned seventh embodiment. The 8-bitdata comprises four higher order bits which represent the LL componentand four lower order bits which represent the vector quantization valueof the high-frequency components. Generally, since a pixel block havinga large intensity change rarely appears in an image, most of the vectorquantization values take small values when the quantization table shownin FIG. 22D is used. Accordingly, in many cases, each of the two mostsignificant bits is “0”, and each of the two least significant bits israndomly either “1” or “0”.

[0400]FIG. 24B is an illustration of 8-bit data corresponding to thenon-edge area obtained in the above-mentioned seventh embodiment. The8-bit data comprises six higher order bits which represent the LLcomponent and two lower order bits which represent the flag information.Each of the two lower order bits among the six higher order bits israndomly either “1”. or “0”, and each of the two lower order bits isalways “0”.

[0401] In the above-mentioned arrangement of bits, the four mostsignificant bits of each of the 8-bit data corresponding to the edgearea and the non-edge area represent the four most significant bits ofthe LL component. These four most significant bits have a goodcorrelation to each other. Each of the fifth and sixth order bitscounted from the most significant bit corresponding to the edge area is“0” in most cases. However, each of the fifth and sixth order bitsrandomly takes either the value “1” or “0”. That is, the goodcorrelation is lost due to low correlation of the non-edge area.Additionally, each of the seventh and eighth order bits counted from themost significant bit has a good correlation in the non-edge area sinceit is always “0” for the non-edge area. However, the seventh and eighthorder bits take randomly either “1” or “0” for the edge area and, thus,correlation is lost due to the randomness for the edge area.

[0402] Accordingly, in the eighth embodiment, the fifth order bit andseventh order bit counted from the most significant bit of the 8-bitdata corresponding to the non-edge area are exchanged, and the sixthorder bit and eighth order bit counted from the most significant bit ofthe 8-bit data corresponding to the non-edge area are exchanged.According to this rearrangement of the order of bits, the codes (8-bitdata) for the edge area and the non-edge area can be in a goodcorrelation. This is because the first to fourth order bits counted fromthe most significant bit for the 8-bit data corresponding to both theedge-area and the non-edge area represent the four most significant bitsof the LL component; each of the fifth and sixth order bits counted fromthe most significant bit is “0” in most cases for both the edge area andnon-edge area; and each of the seventh and eighth order bits countedfrom the most significant bit is randomly either “1” or “0” for both theedge area and non-edge area. As a result, when the data in the pagememory is stored in another memory for sorting, the data can beefficiently compressed by the entropy encoding method.

[0403] A description will now be given, with reference to FIGS. 25 to30, of a ninth embodiment of the present invention. FIG. 25 is a blockdiagram of a printer system which is an image processing systemaccording to the ninth embodiment of the present invention.

[0404] The printer system shown in FIG. 25 comprises an image processingunit 2100, a host unit 2200 such as a personal computer, a hard discdrive unit (HDD) 2300 as a large capacity memory unit and a printerengine 2400.

[0405] The image processing unit 2100 comprises an RIP unit 2101, aframe memory 2102, a block dividing unit 2103, a wavelet transform unit2104, a quantizing unit 2105, an encoding unit 2106, a reverse wavelettransform unit 2107, an edge degree calculating unit 2108 and a gainchanging unit 2109.

[0406] A description will now be given of an operation of theabove-mentioned components of the printer system shown in FIG. 25.

[0407] When image data is input from the host unit 2200 to the RIP unit2101, the RIP unit 2101 transforms the image data to bit map data andstores the bit map data in the frame memory 2102. Then, the bit map datacorresponding to a single page is transferred to the block dividing unit2103. The block dividing unit 2103 divides the bit map data into aplurality of block data each having a predetermined size. The block datais sequentially transferred to the wavelet transform unit 2104. Thewavelet transform unit 2104 transforms the block data to a transformfactor, and sends the transform factor to the quantizing unit 2105 andthe edge degree calculating unit 2108. The edge degree calculating unit2108 calculates a slope of a rate of change in intensity in the blockdata based on the data supplied by the wavelet transform unit 2104. Theresult of the calculation is output to the gain changing unit 2109. Thegain changing unit 109 changes a gain of the quantizing unit 2105 basedon the intensity slope calculated by the edge degree calculating unit2108. The quantizing unit 2105 quantizes the data supplied by thewavelet transform unit 2104 by a gain changed by the gain changing unit2109. The quantized data is transferred to the encoding unit 2106. Theencoding unit 2106 is provided with a buffer memory having a smallcapacity. The encoding unit 2106 performs a data compression (encoding)such as the QM-coder by using the buffer memory. The compressed data issequentially written in the HDD 2300. The compressed data stored in theHDD 2300 is read upon request, and is supplied to the encoding unit2106. Then, the compressed data is expanded by the encoding unit 2106 byusing a reverse encoding method. Thereafter, the quantizing unit 2105restores the expanded data by a reverse quantizing method, and suppliesthe factor to the reverse wavelet transform unit 2107. Accordingly, theimage data is regenerated by the reverse wavelet transform unit 2107,and is output as a visible image by the printer engine 2400.

[0408] The image processing system according to the present embodimentuses a sub-band encoding method for two-dimensional image data.Accordingly, as shown in FIG. 26, the wavelet transform unit 2104 firstseparates a horizontal direction signal of the original image into alow-frequency signal and a high-frequency signal by using a low-passfilter (LPF) 2011 and a high-pass filter (HPF) 2012. Then, a verticalsignal of the original image is subjected to the same process bylow-pass filters 2013 and 2014 and high-pass filters 2015 and 2016.Thus, the original image data is separated into four bands, which are,horizontal high-band (HL), vertical high-band (LH), a diagonal high-band(HH) and a low band (LL) as shown in FIG. 27.

[0409] When the band separation is performed, the original image isdivided into a plurality of 2×2 pixel matrix blocks as shown in FIG. 27.That is, each pixel block comprises two horizontally arranged pixels andtwo vertically arranged pixels. First, the pixels in the pixel block aresubjected to a (2, 2) transformation in the horizontal direction basedon the following equation (11). In this operation, an LPF output s(n)and an HPF output d(n) are obtained. Thereafter, each of the outputs issubjected to a (2, 2) transformation in a vertical direction so that thetransform factor shown in FIG. 27 is obtained. The horizontal high-bandHL represents a high-frequency component of the original image in thehorizontal direction. The vertical high-band LH represents ahigh-frequency component in a vertical direction. The diagonal high-bandHH represents a high-frequency component in a diagonal direction. Thelow band LL represents a low-frequency component.

[0410] It should be noted that the original image may be divided into4×4 matrix pixel blocks. In such a case, each of the components (HL, LH,HH, LL) includes four components.

[0411] The transform factor is restored to image data by a (2, 2)reverse transform based on the following equation (2) after beingsubjected to a quantizing process, an entropy encoding process and areverse quantizing process described later.

[0412] “(2, 2) Transform”

LPF:s(n)=└{X(2n)+X(2n+1)}/2┘

HPF:d(n)=X(2n)−X(2n+1)  (11)

[0413] “(2, 2) Reverse Transform”

X(2n)=s(n)+└{d(n)+1}/2┘

X(2n+1)=s(n)−└d(n)/2┘  (12)

[0414] Generally, a probability density function of high-frequencyfactors is approximated by the Laplacian distribution having a meanvalue of 0. Thus, the quantizing unit 2105 performs a non-linearquantization with respect to high-frequency factors so as to minimizeaverage quantization noise power.

[0415]FIG. 28 is a graph showing a quantizing characteristic of thequantizing unit 2105. Actually, the transform factor takes either a plusvalue or a minus value. Since the plus and minus values are symmetric,only a plus side is shown in the figure. In the example shown in FIG.28, 511 values between −255 and 255 are quantized into 9 values, whichare, −219, −125, −70, −30, 0, 30, 70, 125, 219. These quantizationrepresentative values are compressed by n entropy encoding such as theHuffman encoding.

[0416] The quantizing unit 2105 determines one of ranges into which aninput value falls, the ranges being defined by plurality of thresholdvalues. Then, the quantizing unit 105 outputs one of the quantizationrepresentative values corresponding to the one of the ranges into whichthe input value falls.

[0417] If the gain of the quantizing unit 2105 is set to 1.0, thequantization representative value lies between the two adjacentthreshold values which defines the range. For example, in an exampleshown in FIG. 29B, the quantization representative value 70 is a valuebetween the threshold values 50 and 98. However, if the gain is not 1.0,this condition is not established. That is, in an example shown in FIG.29A in which the gain is set to 2.0 which is greater than 1.0, thequantization representative value 70 is a value within a range between25 and 49. On the other hand, in an example shown in FIG. 29C in whichthe gain is set to 0.5 which is smaller than 1.0, the quantizationrepresentative value 70 is assigned to a range between 101 and 196.

[0418] If the gain of the quantizing unit 2105 is greater than 1.0, anoutput of the quantizing unit 2105 is always greater than an inputthereof. On the contrary, if the gain of the quantizing unit 2105 issmaller than 1.0, the output of the quantizing unit 2105 is alwayssmaller than the input thereof.

[0419] Sharpness of change in gradation of a block is proportional to amagnitude of an absolute value of the high-frequency factor. Thus, ifthe gain of the quantization 2105 with respect to the high-frequencyfactor is set to a value grater than 1.0, a slope of change in intensityof the image restored by a reverse transform becomes steeper. On thecontrary, if the gain is set to a value less than 1.0, the slope becomesgentler.

[0420] If the gradation change in the block is sharp, the absolute valueof the high-frequency factor is increased.

[0421] The high-frequency factor of an area having a sharp gradationchange, such as a character image area or a line image area, takes arelatively large value as shown in FIG. 30A. However, the high-frequencyfactor of an area, in which the gradation change is gentle such as aphotograph image, takes a relatively small value.

[0422] Accordingly, the edge degree calculating unit 2108 calculates adifference d between the largest component and the second largestcomponent among the three components LH, HL and HH of the high-frequencyfactor. The calculated difference d is determined as an edge degree.

[0423] If the 4×4 pixel matrix is used, each of the three components ofthe high-frequency factor includes four components. Thus, one of thefour components having the maximum absolute value is selected as arepresentative of the corresponding one of the three components. Then, adifference d between the largest representative value and the second,largest representative value is calculated.

[0424] As mentioned above, in an area such as a character image area ora line image area, a differential filtering process is performed so asto increase an intensity slope of a contour of the image. On the otherhand, in an area such as a photograph image in which an intensity slopeis gentle, a smoothing filtering process is performed.

[0425] Conventionally, the filtering process for adjusting an imagequality and a quantizing process are performed in different processes.However, in the present embodiment, the filtering process is performedin the data compressing process. This is achieved by the gain changingunit 2109 changing the gain of the quantizing unit 2105 in response othe edge degree calculated by the edge degree calculating unit 2108.Thus, the process time is reduced and a manufacturing cost of the systemcan be reduced. The gain of the quantizing unit 2105 is changed bychanging the threshold values thereof. New threshold values are obtainedby dividing the threshold values at the gain of 1.0 by a desired gain.

[0426] Accordingly, if the desired gain is previously defined by afunction of the edge degree, the new threshold values can be obtainedsequentially in an order of high-frequency factor→edge degree→gain→newthreshold value. Thus, the gain of the quantizing unit 2105 can bechanged to the desired gain by the new threshold values.

[0427] When the gain of the quantizing unit 2105 is changed in responseto a type of image area, there is no need to announce the quantizationtable used when the encoding is performed to the decoding side since thequantization representative value is fixed, that is, not changed. In theconventional technique such as disclosed in the aforementioned patentdocument, information with respect to the used quantization table mustbe stored together with the image data in the compressed data. However,in the present embodiment, such information regarding the quantizationtable is not necessarily stored as compressed data and, thus, thecompression rate is increased.

[0428] If the information is omitted at a part which is not sensitive toa visual sense of human beings, the compression rate can be increasedwithout changing an image quality. For example, a small intensity changeadjacent to an area having a sharp intensity slope is hardly recognized.This effect is known as a mask effect. According to the mask effect,when a difference between edge degrees of adjacent, blocks exceeds apredetermined value, the intensity change of the block having thesmaller edge degree is hardly recognized.

[0429] Accordingly, when a difference between edge degrees of adjacentblocks exceeds a predetermined value, the edge degree calculating unit2108 changes the smaller edge degree to a further smaller value. As aresult, the gain of the quantizing unit 2105 is changed to a smallervalue by the gain changing unit 2109 and, thereby, dispersion of thequantized value is decreased which result in an increase in thecompression rate.

[0430] Additionally, in an area in which mesh point images or lineimages are randomly and densely populated, there may be no problem in avisual sense even when the smoothing process is performed. In manycases, a plurality of components of the high-frequency factor of such anarea may be large values as shown in FIG. 30C.

[0431] In such a case, the edge degree calculating unit 2108 does setthe largest component to the edge degree but set a difference betweenthe largest component and the second largest component to the edgedegree. Thereby, the edge degree is changed to a smaller value. As aresult, the gain of the quantizing unit 2105 is changed to a smallervalue by the gain changing unit 2109 and, thus, dispersion of, thequantized value is decreased which results in an increase in acompression rate.

[0432] A description will now be given, with reference to FIG. 31, of atenth embodiment of the present invention. FIG. 31 is a block diagram ofa printer system which is an image processing system according to thetenth embodiment of the present invention. In FIG. 31, parts that arethe same as the parts shown in FIG. 25 are give the same referencenumerals.

[0433] The printer system shown in FIG. 31 comprises an image processingunit 3100, a host unit 2200 such as a personal computer, a hard discdrive unit (HDD) 2300 as a large capacity memory unit and a printerengine 2400.

[0434] The image processing unit 3100 comprises an RIP unit 2101, aframe memory 2102, a block dividing unit 2103, a wavelet transform unit2104, an image area discriminating unit 3105, a quantizing unit 3106, anencoding unit 2106 and a reverse wavelet transform unit 2107.

[0435] A description will now be given of an operation of theabove-mentioned components of the printer system shown in FIG. 31.

[0436] When image data is input from the host unit 2200 to the RIP unit2101, the RIP unit 2101 transforms the image data to bit map data andstores the bit map data in the frame memory 2102. Then, the bit map datacorresponding to a single page is transferred to the block dividing unit2103. The block dividing unit 2103 divides the bit map data into aplurality of block data each having a predetermined size. The block datais sequentially transferred to the wavelet transform unit 2104. Thewavelet transform unit 2104 transforms the block data to a transformfactor, and sends the transform factor to the image area discriminatingunit 3105 and the quantizing unit 3106. The image area discriminatingunit 3105 determines whether each block corresponds to an edge area or anon-edge area based on the data from the wavelet transform unit 2104.The quantizing unit 3106 quantizes the data from the wavelet transformunit 2104 based on a result of a determination of the image areadiscriminating unit 3105. The quantized data is transferred to theencoding unit 2106. The encoding unit 2106 is provided with a buffermemory having a small capacity. The encoding unit 2106 performs a datacompression (encoding) such as the QM-coder by using the buffer memory.The compressed data is sequentially written in the HDD 2300. Thecompressed data stored in the HDD 2300 is read upon request, and issupplied to the encoding unit 2106. Then, the compressed data isexpanded by the encoding unit 2106 by using a reverse encoding method.Thereafter, the quantizing unit 3106 restores the expanded data by areverse quantizing method, and supplies the factor to the reversewavelet transform unit 2107. Accordingly, the image data is regeneratedby the reverse wavelet transform unit 2107, and is output as a visibleimage by the printer engine 2400.

[0437] The image processing system according to the present embodimentuses a sub-band encoding method for two-dimensional image data. Anoperation of the sub-band encoding method used in this embodiment is thesame as that of the ninth embodiment as shown in FIG. 26, and adescription thereof will be omitted.

[0438] When the band separation is performed, the original image isdivided into a plurality of 2×2 pixel matrix blocks (refer to FIG. 27).That is, each pixel block comprises two horizontally arranged pixels andtwo vertically arranged pixels. First, the pixels in the pixel block aresubjected to a (2, 6) transformation in the horizontal direction basedon the following equations (13). In this operation, an LPF output s(n)and an HPF output d(n) are obtained. Thereafter, each of the outputs issubjected to a (2, 2) transformation in a vertical direction based onthe above-mentioned equation (11) so that the transform factor shown inFIG. 27 is obtained. The horizontal high-band HL represents ahigh-frequency component of the original image in the horizontaldirection. The vertical high-band LH represents a high-frequencycomponent in a vertical direction. The diagonal high-band HH representsa high-frequency component in a diagonal direction. The low band LLrepresents a low-frequency component.

[0439] “(2, 6) Transform”

LPF:s(n)=└{X(2n)+X(2n+1)}/2┘

HPF:d(n)=X(2n)−X(2n+1)+└{(−S(n−1)+s(n+1)+2}/4┘  (13)

[0440] “(2, 6) Reverse Transform

X(2n)=s(n)+└{d(n)−└[−S(n−1)+s(n+1)+2]/4┘}/2┘

X(2n+1)=s(n)−└{d(n)−└[−S(n−1)+s(n+1)+2]/4┘}/2┘  (14)

[0441] The image area discriminating unit 3105 determines whether eachblock corresponds to an edge area or a non-edge area based on thespatial gradation change in the block. If the gradation change is sharp,an absolute value of the high-frequency component is large. That is, ifthe absolute value of the high-frequency component is greater than apredetermined value, is can be determined as an edge area. Otherwise, itcan be determined as a non-edge area. For example, blocks satisfying thefollowing relationship (15) are determined as edge area blocks, andblocks other than the edge area blocks are determined as non-edge areablocks.

31<|HL| or 31<|LH| or 31<|HH|  (15)

[0442] Generally, a probability density function of high-frequencyfactors is approximated by the Laplacian distribution having a meanvalue of 0. Thus, the quantizing unit 3106 performs a non-linearquantization with respect to high-frequency factors so as to minimizeaverage quantization noise power.

[0443]FIG. 32A shows an example of a two-dimensional vector quantizationusing the components HL and LH, the vector quantization beingrepresented by 7 values (3 bits). FIG. 32B shows an example of atwo-dimensional vector quantization using the components HL and LH, thevector quantization being represented by 15 values (4 bits).

[0444] Since the high-frequency component HH has less importance in avisual sense, the high-frequency component is discarded in the presentembodiment. Additionally, the probability density function of thelow-frequency component LL fluctuates for images, and there is nocorrelation. Thus, the probability density function of the low-frequencycomponent LL is regarded as a uniform distribution, and is subjected toa linear quantization. If the component LL is 8 bits and a 3-bitquantization is performed, five least significant bits are deleted. If a4-bit quantization is performed, four least significant bits aredeleted.

[0445] The quantizing unit 3106 changes a ratio of numbers of bitsassigned to the high-frequency component and the low-frequency componentwhile the number of bits assigned to a single block is maintained to bethe same. That is, a greater number of bits are deleted from thehigh-frequency component of the edge area, whereas a smaller number ofbits are deleted from the high-frequency component of the edge area.However, the number of bits of the high-frequency component of the edgearea is equal to the number of bits of the low, frequency component ofthe non-edge area.

[0446]FIG. 33 is an illustration for explaining an example of a bitarrangement for each area. In the example shown in FIG. 33, 8 bits arealways assigned to a single block. For the edge area, 4 bits areassigned to the high-frequency component, 3 bits to the low-frequencycomponent, and 1 bit to area information. For the non-edge area, 3 bitsare assigned to the high-frequency component, 4 bits to thelow-frequency component, and 1 bit to area information.

[0447] As mentioned above, the encoding unit 2106 produces data forcompensating errors so as to produce an image having higher image bycomplementing the data represented by a quantization factor having afixed length for a single block. For example, a difference between theoriginal image and the restored image is previously obtained, therestored image being obtained from the quantizing factor having a fixedlength by being subjected to a reverse transform using the equations(12) and (14). Then, the quantization factor and data (error data)corresponding to the above-mentioned difference are encoded by anentropy encoding method.

[0448] The above-mentioned quantization factor and the error data arecompressed (encoded) by a variable length reverse encoding method suchas the QM-coder. When an image is rotated by a printer, data structureis preferably a fixed length. Thus, in such a case, a rotation of theimage is performed prior to the entropy encoding.

[0449] As mentioned above, the compressed data read from the HDD 2300 isreverse encoded (decoded) by the encoding unit 2106, and is reversequantized by the quantizing unit 3106 so as to return to the transformfactor., The transform factor is subjected to a reverse transform by thereverse wavelet transform unit 2107 so as to restore the image data. Atthis time, both the quantization factor and the error data are decodedso as to restore the transform factor. Then, the transform factor issubjected to reverse transform and, thereby, the original image can bealmost completely restored. Additionally, an image having an imagequality at a certain level can be restored by reverse transforming thequantized factor having a fixed length, that is, both the high-frequencycomponent and the low-frequency component.

[0450] Additionally, when only the high-frequency component of thequantized factors with a fixed length is reverse transformed for theedge area, and when only the low-frequency component of the quantizedfactor with a fixed length is reverse transformed for the non-edge area,image quality is considerably deteriorated, but a feature of theoriginal image can be still maintained. Such a restored image can beused for a trial printing.

[0451] Further, if the decoding process of the image is not performedfor all blocks but for every other block as shown in FIG. 34, imagequality and resolution may be deteriorated, but the restored image canstill provide a certain feature of the original image. Such a restoredimage can be used as an icon on a display unit.

[0452] The present invention is not limited to the specificallydisclosed embodiments, and variations and modifications may be madewithout departing from the scope of the present invention.

[0453] The present application is based on Japanese priorityapplications No. 9-118207 filed on May 8, 1997, No. 9-156006 filed onMay 29, 1997 and No. 9-156007 file on May 29, 1998, the entire contentsof which are hereby incorporated by reference.

What is claimed is:
 1. An image processing system comprising: a bufferunit extracting n×m pixel matrix block data from image data, where n andm are integers; a sub-band transform unit transforming the n×m pixelmatrix block data by a sub-band transform method so as to obtain atransform factor having a plurality of frequency components; and aquantizing unit quantizing the transform factor by a fixed-lengthquantizing method by deleting a predetermined number of lower order bitsof each of the frequency components.
 2. An image processing systemcomprising: a buffer unit extracting n×m pixel matrix block data fromimage data, where n and m are integers; a sub-band transform unittransforming the n×m pixel matrix block data by a sub-band transformmethod so as to obtain a transform factor having a plurality offrequency components including a low-frequency component and ahigh-frequency component; an area discriminating unit discriminating atype of an image area corresponding to the n×m pixel matrix block databeing processed so that the image area is determined as one of an edgearea and a non-edge area, a discrimination being made based on whetheror not an absolute value of each of the components of the transformfactor exceeds a threshold value; and a quantizing unit quantizing thetransform factor by a fixed-length quantizing method by deleting apredetermined number of lower order bits of each of the frequencycomponents, the number of deleted lower order bits of each of thefrequency components of the transform factor being changed in accordancewith a type of image area being processed so that image data includingthe transform factor and flag information indicating a type of imagearea has a predetermined fixed length.
 3. The image processing system asclaimed in claim 2, wherein said quantizing unit deletes lower orderbits of each of the low-frequency component and the high-frequencycomponent so that a number of deleted lower order bits for the edge areais greater than a number of deleted lower order bits for the non-edgearea.
 4. The image processing system as claimed in claim 2, wherein saidquantizing unit quantizes the high-frequency component by a vectorquantizing method.
 5. The image processing system as claimed in claim 2,wherein said quantizing unit embeds the flag information into thetransform factor.
 6. The image processing system as claimed in claim 5,wherein said quantizing unit changes bit data representing the transformfactor so that the flag information is represented by a part of the databits representing the transform factor.
 7. The image processing systemas claimed in claim 2, wherein said quantizing unit changes thetransform factor so that correlation between the transform factors ofdifferent types is increased.
 8. The image processing system as claimedin claim 7, wherein said quantizing unit changes a bit arrangement ofthe transform factor so that correlation between the transform factorcorresponding to the edge area and the transform factor corresponding tothe non-edge area is increased.
 9. An image processing method comprisingthe steps of: extracting n×m pixel matrix block data from image data,where n and m are integers; transforming the n×m pixel matrix block databy a sub-band transform method so as to obtain a transform factor havinga plurality of frequency components; and quantizing the transform factorby a fixed-length quantizing method by deleting a predetermined numberof lower order bits of each of the frequency components.
 10. An imageprocessing method comprising the steps of: extracting n×m pixel matrixblock data from image data, where n and m are integers; transforming then×m pixel matrix block data by a sub-band transform method so as toobtain a transform factor having a plurality of frequency componentsincluding a low-frequency component and a high-frequency component;discriminating a type of an image area corresponding to the n×m pixelmatrix block data being processed so that the image area is determinedas one of an edge area and a non-edge area, a discrimination being madebased on whether or not an absolute value of each of the components ofthe transform factor exceeds a, threshold value; and quantizing thetransform factor by a fixed-length quantizing method by deleting apredetermined number of lower order bits of each of the frequencycomponents, the number of deleted lower order bits of each of thefrequency components of the transform factor being changed in accordancewith, a type of image area being processed so that image data includingthe transform factor and flag information indicating a type of imagearea has a predetermined fixed length.
 11. An image processing systemcomprising: a buffer unit extracting n×m pixel matrix block data fromimage data, where n and m are integers; a binarizing unit transformingthe n×m pixel matrix block data into binary data represented by amaximum value and a minimum value; a differential data calculating unitcalculating differential data which is a difference between a value ofeach pixel in the n×m pixel matrix block data and one of the maximumvalue and the minimum value of the binary data; a sub-band transformunit transforming the differential data by a sub-band transform methodso as to obtain a transform factor having a plurality of frequencycomponents; and an encoding unit encoding the binary data and thesub-band transform factor so as to obtain a code representing the imagedata.
 12. The image processing system as claimed in claim 11, whereinsaid encoding unit deletes lower order bits of the sub-band transformfactor so that the code has a predetermined fixed length.
 13. The imageprocessing system as claimed in claim 12, wherein said encoding unitdeletes more lower order bits from the high-frequency component than thelow-frequency component when both the maximum value and the minimumvalue exist in the binary data of the same block data.
 14. The imageprocessing system as claimed in claim 11, wherein said encoding unitquantizes the high-frequency component of the sub-band transform factorby a vector quantizing method.
 15. An image processing method comprisingthe steps of: extracting n×m pixel matrix block data from image data,where n and m are integers; transforming the n×m pixel matrix block datainto binary data represented by a maximum value and a minimum value;calculating differential data which is a difference between a value ofeach pixel in the n×m pixel matrix block data and one of the maximumvalue and the minimum value of the binary data; transforming thedifferential data by a sub-band transform method so as to obtain atransform factor having a plurality of frequency components; andencoding the binary data and the sub-band transform factor so as toobtain a code representing the image data.
 16. An image processingsystem comprising: a dividing unit dividing image data into a pluralityof n×m pixel matrix block data, where n and m are integers; a transformunit transforming each pixel in the n×m pixel matrix block data by afrequency transform method so as to produce a transform factor includinga high-frequency component and a low-frequency component; an image areadiscriminating unit for determining whether the block being processedcorresponds to an edge area or a non-edge area based on the transformfactor output from said transform unit; a quantizing unit quantizing thetransform factor for the edge area and the transform factor for thenon-edge area by different methods; and an encoding unit encoding anoutput of said quantizing unit by an entropy encoding method, wherein atotal of a number of bits of the high-frequency component and a numberof bits of the low-frequency is the same regardless of types of the edgearea or the non-edge area, and a number of bits of the high-frequencycomponent for the edge area is the same as a number of bits of thelow-frequency component of the non-edge area.
 17. The image processingsystem as claimed in claim 16, wherein said encoding unit encodes errordata generated by said quantizing unit.
 18. The image processing systemas claimed in, claim 16, wherein an encoding of the image for the edge,area is performed by using only the high-frequency component, and anencoding of the image for the non-edge area is performed by using onlythe low-frequency component.
 19. The image processing system as claimedin claim 16, wherein every other block data is used for restoring anoriginal image.
 20. An image processing method comprising the steps of:dividing image data into a plurality of n×m pixel matrix block data,where n and m are integers; transforming each pixel in the n×m pixelmatrix block data by a frequency transform method so as to produce atransform factor including a high-frequency component and alow-frequency component; determining Whether the block being processedcorresponds to an edge area or a non-edge area based on the transformfactor output from said transform unit; quantizing the transform factorfor the edge area and the transform factor for the non-edge area bydifferent methods; and encoding an output of said quantizing unit by anentropy encoding method, wherein a total of a number of bits of thehigh-frequency component and a number of bits of the low-frequency isthe same regardless of types of the edge area or the non-edge area, anda number of bits of the high-frequency component for the edge area isthe same as a number of bits of the low-frequency component of thenon-edge area.